OpenRaiser/Intern-Atlas huggingface dataset:
Static Analysis for Detecting Transaction Conflicts in Ethereum Smart Contracts
Venue: arXiv cs.DC 2025
Year: 2025

Ethereum smart contracts operate in a concurrent environment where multiple transactions can be submitted simultaneously. However, the Ethereum Virtual Machine (EVM) enforces sequential execution of transactions within each block to prevent conflicts arising from concurrent access to the same state variables. Although this approach guarantees correct behavior, it limits the ability of validators to leverage multi-core architectures for faster transaction processing, thus restricting throughput. Existing solutions introduce concurrency by allowing simultaneous transaction execution combined with runtime conflict detection and rollback mechanisms to maintain correctness. However, these methods incur significant overhead due to continuous conflict tracking and transaction reversion. Recently, alternative approaches have emerged that aim to predict conflicts statically, before execution, by analyzing smart contract code for potential transaction interactions. Despite their promise, there is a lack of comprehensive studies that examine static conflict detection and its broader implications in specific smart contracts. This paper fills this important gap by proposing a novel static analysis method to detect potential transaction conflicts in Ethereum smart contracts. Our method identifies read-write, write-write, and function call conflicts between transaction pairs by analyzing state variable access patterns in Solidity contracts. We implement a tool that parses contract code and performs conflict detection. Evaluation on a dataset of real-world Ethereum smart contracts demonstrates that our approach achieves high precision in identifying potential conflicts. By enabling proactive conflict detection, our tool supports further design of transaction scheduling strategies that reduce runtime failures, enhance validator throughput, and contribute to blockchain scalability.
Information-theoretic Quantification of High-order Feature Effects in Classification Problems
Venue: arXiv cs.LG 2025
Year: 2025

Understanding the contribution of individual features in predictive models remains a central goal in interpretable machine learning, and while many model-agnostic methods exist to estimate feature importance, they often fall short in capturing high-order interactions and disentangling overlapping contributions. In this work, we present an information-theoretic extension of the High-order interactions for Feature importance (Hi-Fi) method, leveraging Conditional Mutual Information (CMI) estimated via a k-Nearest Neighbor (kNN) approach working on mixed discrete and continuous random variables. Our framework decomposes feature contributions into unique, synergistic, and redundant components, offering a richer, model-independent understanding of their predictive roles. We validate the method using synthetic datasets with known Gaussian structures, where ground truth interaction patterns are analytically derived, and further test it on non-Gaussian and real-world gene expression data from TCGA-BRCA. Results indicate that the proposed estimator accurately recovers theoretical and expected findings, providing a potential use case for developing feature selection algorithms or model development based on interaction analysis.
WebSynthesis: World-Model-Guided MCTS for Efficient WebUI-Trajectory Synthesis
Venue: arXiv cs.AI 2025
Year: 2025

Recent advancements in large language models (LLMs) have significantly improved the capabilities of web agents. However, effectively navigating complex and dynamic web environments still requires more advanced trajectory-level planning and execution. Prior studies have addressed self-improving agents by collecting extensive GUI trajectories from real-environment interactions. Despite their effectiveness, these approaches encounter two critical challenges: (1) Uncontrollable environment states, where real or sandboxed web environments often yield unstable and non-deterministic feedback, complicating the reproduction and debugging of agent behaviors; and (2) High API costs, as generating even a single interaction trajectory can involve hundreds of queries, leading to considerable API usage and computational expenses. To address these limitations and enable scalable self-improvement for agents, we propose WebSynthesis, a novel framework for trajectory synthesis and training. WebSynthesis leverages a learned world model to simulate virtual web environments, allowing a policy agent to perform efficient and reversible tree-based planning. This approach supports the large-scale generation of diverse and high-quality trajectories, which are subsequently utilized to refine the agent's policy. Experimental results demonstrate that an agent trained using WebSynthesis on a small-scale synthetic dataset achieves performance comparable to or even surpassing that of models trained on large-scale real-world data.
Inverse Reinforcement Learning using Revealed Preferences and Passive Stochastic Optimization
Venue: arXiv cs.LG 2025
Year: 2025

This monograph, spanning three chapters, explores Inverse Reinforcement Learning (IRL). The first two chapters view inverse reinforcement learning (IRL) through the lens of revealed preferences from microeconomics while the third chapter studies adaptive IRL via Langevin dynamics stochastic gradient algorithms. Chapter uses classical revealed preference theory (Afriat's theorem and extensions) to identify constrained utility maximizers based on observed agent actions. This allows for the reconstruction of set-valued estimates of an agent's utility. We illustrate this procedure by identifying the presence of a cognitive radar and reconstructing its utility function. The chapter also addresses the construction of a statistical detector for utility maximization behavior when agent actions are corrupted by noise. Chapter 2 studies Bayesian IRL. It investigates how an analyst can determine if an observed agent is a rationally inattentive Bayesian utility maximizer (i.e., simultaneously optimizing its utility and observation likelihood). The chapter discusses inverse stopping-time problems, focusing on reconstructing the continuation and stopping costs of a Bayesian agent operating over a random horizon. We then apply this IRL methodology to identify the presence of a Bayes-optimal sequential detector. Additionally, Chapter 2 provides a concise overview of discrete choice models, inverse Bayesian filtering, and inverse stochastic gradient algorithms for adaptive IRL. Finally, Chapter 3 introduces an adaptive IRL approach utilizing passive Langevin dynamics. This method aims to track time-varying utility functions given noisy and misspecified gradients. In essence, the adaptive IRL algorithms presented in Chapter 3 can be conceptualized as inverse stochastic gradient algorithms, as they learn the utility function in real-time while a stochastic gradient algorithm is in operation.
MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture
Venue: arXiv cs.CV 2025
Year: 2025

Hyperspectral image (HSI) classification faces challenges such as high-dimensional data, limited training samples, and spectral redundancy, which often lead to overfitting and insufficient generalization capability. This paper proposes a novel MVNet network architecture that integrates 3D-CNN's local feature extraction, Transformer's global modeling, and Mamba's linear complexity sequence modeling capabilities, achieving efficient spatial-spectral feature extraction and fusion. MVNet features a redesigned dual-branch Mamba module, including a State Space Model (SSM) branch and a non-SSM branch employing 1D convolution with SiLU activation, enhancing modeling of both short-range and long-range dependencies while reducing computational latency in traditional Mamba. The optimized HSI-MambaVision Mixer module overcomes the unidirectional limitation of causal convolution, capturing bidirectional spatial-spectral dependencies in a single forward pass through decoupled attention that focuses on high-value features, alleviating parameter redundancy and the curse of dimensionality. On IN, UP, and KSC datasets, MVNet outperforms mainstream hyperspectral image classification methods in both classification accuracy and computational efficiency, demonstrating robust capability in processing complex HSI data.
Multimedia Verification Through Multi-Agent Deep Research Multimodal Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

This paper presents our submission to the ACMMM25 - Grand Challenge on Multimedia Verification. We developed a multi-agent verification system that combines Multimodal Large Language Models (MLLMs) with specialized verification tools to detect multimedia misinformation. Our system operates through six stages: raw data processing, planning, information extraction, deep research, evidence collection, and report generation. The core Deep Researcher Agent employs four tools: reverse image search, metadata analysis, fact-checking databases, and verified news processing that extracts spatial, temporal, attribution, and motivational context. We demonstrate our approach on a challenge dataset sample involving complex multimedia content. Our system successfully verified content authenticity, extracted precise geolocation and timing information, and traced source attribution across multiple platforms, effectively addressing real-world multimedia verification scenarios.
SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights
Venue: arXiv cs.CV 2025
Year: 2025

With the rise and development of computer vision and LLMs, intelligence is everywhere, especially for people and cars. However, for tremendous food attributes (such as origin, quantity, weight, quality, sweetness, etc.), existing research still mainly focuses on the study of categories. The reason is the lack of a large and comprehensive benchmark for food. Besides, many food attributes (such as sweetness, weight, and fine-grained categories) are challenging to accurately percept solely through RGB cameras. To fulfill this gap and promote the development of intelligent food analysis, in this paper, we built the first large-scale spectral food (SFOOD) benchmark suite. We spent a lot of manpower and equipment costs to organize existing food datasets and collect hyperspectral images of hundreds of foods, and we used instruments to experimentally determine food attributes such as sweetness and weight. The resulting benchmark consists of 3,266 food categories and 2,351 k data points for 17 main food categories. Extensive evaluations find that: (i) Large-scale models are still poor at digitizing food. Compared to people and cars, food has gradually become one of the most difficult objects to study; (ii) Spectrum data are crucial for analyzing food properties (such as sweetness). Our benchmark will be open source and continuously iterated for different food analysis tasks.
Resource-Efficient Seamless Transitions For High-Performance Multi-hop UAV Multicasting
Venue: arXiv cs.NI 2025
Year: 2025

Many UAV-related applications require group communications between UAVs to reliably and efficiently deliver rich media content as well as to extend line-of-sight coverage between sky and ground. This paper studies fast yet resource-efficient UAV transitions while maintaining high multicasting performance. We develop a set of analytic and algorithmic results to form the efficient transition formation (ETF) algorithm that deals with different UAV transition scenarios in a multicasting environment. The ETF algorithm first evaluates the seamlessness of a straight-line trajectory (SLT), by processing low-complexity computations (e.g., Euclidean distances) or a chain of fast checks with controlled traffic overheads. For an interrupted SLT, ETF establishes a new trajectory consisting of a minimum number of seamless straight lines that join at specially selected locations in terms of controlling mobile UAVs' seamless travel distances. Our simulation studies quantify the multicasting performance gains that ETF allows, outperforming compared studies when seamlessly transiting UAV group members.
Learning Software Bug Reports: A Systematic Literature Review
Venue: arXiv cs.SE 2025
Year: 2025

The recent advancement of artificial intelligence, especially machine learning (ML), has significantly impacted software engineering research, including bug report analysis. ML aims to automate the understanding, extraction, and correlation of information from bug reports. Despite its growing importance, there has been no comprehensive review in this area. In this paper, we present a systematic literature review covering 1,825 papers, selecting 204 for detailed analysis. We derive seven key findings: 1) Extensive use of CNN, LSTM, and $k$NN for bug report analysis, with advanced models like BERT underutilized due to their complexity. 2) Word2Vec and TF-IDF are popular for feature representation, with a rise in deep learning approaches. 3) Stop word removal is the most common preprocessing, with structural methods rising after 2020. 4) Eclipse and Mozilla are the most frequently evaluated software projects. 5) Bug categorization is the most common task, followed by bug localization and severity prediction. 6) There is increasing attention on specific bugs like non-functional and performance bugs. 7) Common evaluation metrics are F1-score, Recall, Precision, and Accuracy, with $k$-fold cross-validation preferred for model evaluation. 8) Many studies lack robust statistical tests. We also identify six promising future research directions to provide useful insights for practitioners.
TeleSim: A Network-Aware Testbed and Benchmark Dataset for Telerobotic Applications
Venue: arXiv cs.NI 2025
Year: 2025

Telerobotic technologies are becoming increasingly essential in fields such as remote surgery, nuclear decommissioning, and space exploration. Reliable datasets and testbeds are essential for evaluating telerobotic system performance prior to real-world deployment. However, there is a notable lack of datasets that capture the impact of network delays, as well as testbeds that realistically model the communication link between the operator and the robot. This paper introduces TeleSim, a network-aware teleoperation dataset and testbed designed to assess the performance of telerobotic applications under diverse network conditions. TeleSim systematically collects performance data from fine manipulation tasks executed under three predefined network quality tiers: High, Medium, and Low. Each tier is characterized through controlled settings of bandwidth, latency, jitter, and packet loss. Using OMNeT++ for precise network simulation, we record a wide range of metrics, including completion time, success rates, video quality indicators (Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM)), and quality of service (QoS) parameters. TeleSim comprises 300 experimental trials, providing a robust benchmark for evaluating teleoperation systems across heterogeneous network scenarios. In the worst network condition, completion time increases by 221.8% and success rate drops by 64%. Our findings reveal that network degradation leads to compounding negative impacts, notably reduced video quality and prolonged task execution, highlighting the need for adaptive, resilient teleoperation protocols. The full dataset and testbed software are publicly available on our GitHub repository: https://github.com/ConnectedRoboticsLab and YouTube channel: https://youtu.be/Fz_1iOYe104.
MedGellan: LLM-Generated Medical Guidance to Support Physicians
Venue: arXiv cs.AI 2025
Year: 2025

Medical decision-making is a critical task, where errors can result in serious, potentially life-threatening consequences. While full automation remains challenging, hybrid frameworks that combine machine intelligence with human oversight offer a practical alternative. In this paper, we present MedGellan, a lightweight, annotation-free framework that uses a Large Language Model (LLM) to generate clinical guidance from raw medical records, which is then used by a physician to predict diagnoses. MedGellan uses a Bayesian-inspired prompting strategy that respects the temporal order of clinical data. Preliminary experiments show that the guidance generated by the LLM with MedGellan improves diagnostic performance, particularly in recall and $F_1$ score.
Adapter-state Sharing CLIP for Parameter-efficient Multimodal Sarcasm Detection
Venue: arXiv cs.CL 2025
Year: 2025

The growing prevalence of multimodal image-text sarcasm on social media poses challenges for opinion mining systems. Existing approaches rely on full fine-tuning of large models, making them unsuitable to adapt under resource-constrained settings. While recent parameter-efficient fine-tuning (PEFT) methods offer promise, their off-the-shelf use underperforms on complex tasks like sarcasm detection. We propose AdS-CLIP (Adapter-state Sharing in CLIP), a lightweight framework built on CLIP that inserts adapters only in the upper layers to preserve low-level unimodal representations in the lower layers and introduces a novel adapter-state sharing mechanism, where textual adapters guide visual ones to promote efficient cross-modal learning in the upper layers. Experiments on two public benchmarks demonstrate that AdS-CLIP not only outperforms standard PEFT methods but also existing multimodal baselines with significantly fewer trainable parameters.
The planar edge-coloring theorem of Vizing in $O(n\log n)$ time
Venue: arXiv cs.DS 2025
Year: 2025

In 1965, Vizing [Diskret. Analiz, 1965] showed that every planar graph of maximum degree $Δ\ge 8$ can be edge-colored using $Δ$ colors. The direct implementation of the Vizing's proof gives an algorithm that finds the coloring in $O(n^2)$ time for an $n$-vertex input graph. Chrobak and Nishizeki [J. Algorithms, 1990] have shown a more careful algorithm, which improves the time to $O(n\log n)$ time, though only for $Δ\ge 9$. In this paper, we extend their ideas to get an algorithm also for the missing case $Δ=8$. To this end, we modify the original recoloring procedure of Vizing. This generalizes to bounded genus graphs.
Grounded Gesture Generation: Language, Motion, and Space
Venue: arXiv cs.CV 2025
Year: 2025

Human motion generation has advanced rapidly in recent years, yet the critical problem of creating spatially grounded, context-aware gestures has been largely overlooked. Existing models typically specialize either in descriptive motion generation, such as locomotion and object interaction, or in isolated co-speech gesture synthesis aligned with utterance semantics. However, both lines of work often treat motion and environmental grounding separately, limiting advances toward embodied, communicative agents. To address this gap, our work introduces a multimodal dataset and framework for grounded gesture generation, combining two key resources: (1) a synthetic dataset of spatially grounded referential gestures, and (2) MM-Conv, a VR-based dataset capturing two-party dialogues. Together, they provide over 7.7 hours of synchronized motion, speech, and 3D scene information, standardized in the HumanML3D format. Our framework further connects to a physics-based simulator, enabling synthetic data generation and situated evaluation. By bridging gesture modeling and spatial grounding, our contribution establishes a foundation for advancing research in situated gesture generation and grounded multimodal interaction. Project page: https://groundedgestures.github.io/
Verification of Visual Controllers via Compositional Geometric Transformations
Venue: arXiv cs.RO 2025
Year: 2025

Perception-based neural network controllers are increasingly used in autonomous systems that rely on visual inputs to operate in the real world. Ensuring the safety of such systems under uncertainty is challenging. Existing verification techniques typically focus on Lp-bounded perturbations in the pixel space, which fails to capture the low-dimensional structure of many real-world effects. In this work, we introduce a novel verification framework for perception-based controllers that can generate outer-approximations of reachable sets through explicitly modeling uncertain observations with geometric perturbations. Our approach constructs a boundable mapping from states to images, enabling the use of state-based verification tools while accounting for uncertainty in perception. We provide theoretical guarantees on the soundness of our method and demonstrate its effectiveness across benchmark control environments. This work provides a principled framework for certifying the safety of perception-driven control systems under realistic visual perturbations.
VLM-TDP: VLM-guided Trajectory-conditioned Diffusion Policy for Robust Long-Horizon Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Diffusion policy has demonstrated promising performance in the field of robotic manipulation. However, its effectiveness has been primarily limited in short-horizon tasks, and its performance significantly degrades in the presence of image noise. To address these limitations, we propose a VLM-guided trajectory-conditioned diffusion policy (VLM-TDP) for robust and long-horizon manipulation. Specifically, the proposed method leverages state-of-the-art vision-language models (VLMs) to decompose long-horizon tasks into concise, manageable sub-tasks, while also innovatively generating voxel-based trajectories for each sub-task. The generated trajectories serve as a crucial conditioning factor, effectively steering the diffusion policy and substantially enhancing its performance. The proposed Trajectory-conditioned Diffusion Policy (TDP) is trained on trajectories derived from demonstration data and validated using the trajectories generated by the VLM. Simulation experimental results indicate that our method significantly outperforms classical diffusion policies, achieving an average 44% increase in success rate, over 100% improvement in long-horizon tasks, and a 20% reduction in performance degradation in challenging conditions, such as noisy images or altered environments. These findings are further reinforced by our real-world experiments, where the performance gap becomes even more pronounced in long-horizon tasks. Videos are available on https://youtu.be/g0T6h32OSC8
da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs
Venue: arXiv cs.AR 2025
Year: 2025

Neural networks with a latency requirement on the order of microseconds, like the ones used at the CERN Large Hadron Collider, are typically deployed on FPGAs fully unrolled and pipelined. A bottleneck for the deployment of such neural networks is area utilization, which is directly related to the required constant matrix-vector multiplication (CMVM) operations. In this work, we propose an efficient algorithm for implementing CMVM operations with distributed arithmetic (DA) on FPGAs that simultaneously optimizes for area consumption and latency. The algorithm achieves resource reduction similar to state-of-the-art algorithms while being significantly faster to compute. The proposed algorithm is open-sourced and integrated into the \texttt{hls4ml} library, a free and open-source library for running real-time neural network inference on FPGAs. We show that the proposed algorithm can reduce on-chip resources by up to a third for realistic, highly quantized neural networks while simultaneously reducing latency, enabling the implementation of previously infeasible networks.